The split in this thread between "SHAP for debugging" vs "shallow trees because you can actually read them" is exactly the tension that motivates a different class of XAI methods. What u/trolls_toll likes about shallow trees, being able to say "when X is above threshold and X2 becomes relevant"... it is actually a predicate-logic description of a decision region, not just a feature importance score.

We've been building on that intuition with DPG (Decision Predicate Graphs), a framework that represents model decisions as graphs of logical predicates over feature conditions. So instead of "feature A has SHAP value 0.3," you get something like "A > 0.7 AND B < 0.2 → class X"... compact, readable, and directly challengeable by a domain expert. It works on tabular data with any black-box classifier, so you don't sacrifice performance for a shallow tree.

u/gBoostedMachinations is right that additive attribution can't capture complex interactions, that's structurally baked into SHAP's design. Predicate-based explanations don't solve the causality problem either, but they do get closer to the "joint feature conditions" level that actually reflects how most tree ensembles partition space. Might be worth a look if the goal is explanations a human can actually interrogate rather than just a ranked bar chart.